---
title: "geoCat"
output:
flexdashboard::flex_dashboard:
vertical_layout: scroll
orientation: rows
social: menu
source_code: embed
---
# Cat US
Use these results without warranty or express or implied fitness for any purpose. The results are not vetted for accuracy. The results are not vetted for completeness. The results are not vetted for usability
- Limited to results from IACat and RyeCat
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
library(data.table)
library(highcharter)
library(dplyr)
library(knitr)
library(maps)
library(plotly)
data("county.fips")
data("uscountygeojson")
data("usgeojson")
theme_set(theme_minimal())
#the COUNTYFP column is a character column, so we don't want to convert it to numeric
# df <- fread("../output/output_county_count.csv_combined.csv", colClasses = c("COUNTYFP" = "character"))
df <-
fread(
"../output/output_state_count.csv_combined.csv",
colClasses = c("COUNTYFP" = "character")
)
# create a vector of the names that need to be mapped
namesToMap <- c("Rye8", "RyePhone", "Rye13")
# replace the names in the Name column with "RyeCat"
# if the names are not in the Name column, throw a warning
if(any(!namesToMap %in% df$Name)) warning("Some names are not in the data frame")
df$OriginalName <- df$Name
df$Name[df$Name %in% namesToMap] <- "RyeCat"
# get the names that start with sofia or that contain Papa or that start with tonga
tongaNames <- df$Name[grepl("^tonga", df$Name, ignore.case = TRUE)]
papaNames <- df$Name[grepl("papa", df$Name, ignore.case = TRUE)]
sofiaNames <- df$Name[grepl("^sofia", df$Name, ignore.case = TRUE)]
IACatNames <- c(tongaNames, papaNames, sofiaNames, "iha")
iaCatPatterns= c("^tonga", "papa", "^sofia", "iha")
# map the IACatNames to IACat
df$Name[df$Name %in% IACatNames] <- "IACat"
# print the unique names that are not RyeCat or IACat
# kable(table()
missingNames <- unique(df$Name[!df$Name %in% c("RyeCat", "IACat")])
# subset the data frame to only include the rows where the Name column is equal to "RyeCat" or "IACat"
df <- df[Name %in% c("RyeCat", "IACat"), ]
```
```{r echo=FALSE, message=FALSE, warning=FALSE}
# function to sum the counts column by a given column name
sum_by_column <- function(in_df, column_names) {
#group by the list of column names and sum the counts column
in_df <- in_df[, .(counts = sum(counts)), by = column_names]
return(in_df)
}
```
## State counts
### Total Cats per State
```{r}
sumByState <-
sum_by_column(in_df = df, column_names = c("STATE_NAME"))
# log10 transform the counts column
sumByState$counts <- log10(sumByState$counts)
#create a chlorealpleth map of the US
highchart() %>%
hc_add_series_map(
usgeojson,
sumByState,
value = "counts",
joinBy = c("name", "STATE_NAME"),
name = "Count",
dataLabels = list(enabled = TRUE, format = '{point.name}')
) %>%
hc_mapNavigation(enabled = TRUE) %>%
hc_colorAxis(stops = color_stops()) %>%
hc_title(text = "Count of Tracks by State") %>%
hc_add_theme(hc_theme_smpl()) %>%
hc_legend(enabled = TRUE) %>%
hc_tooltip(pointFormat = "{point.name}: {point.value}")
```
### Count of Tracks by County
```{r}
# TODO where is California counties
df$COUNTY_FIP=paste0(df$STATEFP,df$COUNTYFP)
sumByCounty <-
sum_by_column(in_df = df, column_names = c("COUNTY_FIP"))
# log10 transform the counts column
sumByCounty$counts <- log10(sumByCounty$counts)
#create a chlorealpleth map of the US
highchart() %>%
hc_add_series_map(
uscountygeojson,
sumByCounty,
value = "counts",
joinBy = c("fips", "COUNTY_FIP"),
name = "Count") %>%
hc_mapNavigation(enabled = TRUE) %>%
hc_colorAxis(stops = color_stops()) %>%
hc_title(text = "Count of Tracks by County") %>%
hc_add_theme(hc_theme_smpl()) %>%
hc_legend(enabled = FALSE)
# %>%
# hc_tooltip(pointFormat = "{point.name}: {point.value}")
```
## Cat wins
### cat counts per state
```{r}
# sum the counts column by the Name and STATE_NAME columns
sumByCatStateFull <-
sum_by_column(in_df = df, column_names = c("Name", "STATE_NAME"))
sumByCatStateWin <-
sumByCatStateFull[, .SD[which.max(counts)], by = STATE_NAME]
# create a color palette that maps RyeCat to bl
colorPalette <- c("#0000FF","#FF0000")
# create a named vector of colors
colorVector <- setNames(colorPalette, unique(sumByCatStateWin$Name))
# replace the color column with the color vector
sumByCatStateWin$color <- colorVector[sumByCatStateWin$Name]
# log10 transform the counts column
#create a chlorealpleth map of the US
highchart() %>%
hc_add_series_map(
usgeojson,
sumByCatStateWin,
value = "counts",
joinBy = c("name", "STATE_NAME"),
name = "Cat Winner",
dataLabels = list(enabled = TRUE, format = '{point.name}'),color =colorVector
) %>%
hc_mapNavigation(enabled = TRUE) %>%
hc_title(text = "Which Cat Wins Which State (+DC") %>%
hc_add_theme(hc_theme_smpl()) %>%
hc_legend(enabled = FALSE) %>%
hc_tooltip(pointFormat = "{point.name} is won by {point.Name}: {point.value} total tracks")
```
### States Won per Cat {data-width=250}
```{r}
# count the number of states that each cat won
total = sumByCatStateWin[, .N, by = Name]
#create a ggplot bar chart of the number of states that each cat won
# label by the cat name
# color by the colorPalette
# order by the number of states won
g = ggplot(total, aes(x = Name, y = N, fill = Name)) +
geom_bar(stat = "identity") +
geom_text(aes(label = Name), vjust = -0.5, size = 3) +
scale_fill_manual(values = colorVector) +
labs(x = "Cat", y = "Number of States(+DC) Won") +
theme_minimal() +
theme(legend.position = "none")
ggplotly(g)
```
### cat counts per county
```{r}
# sum the counts column by the Name and STATE_NAME columns
sumByCatCountyFull <-
sum_by_column(in_df = df,
column_names = c("Name", "COUNTY_FIP"))
sumByCatCountyWin <-
sumByCatCountyFull[, .SD[which.max(counts)], by = COUNTY_FIP]
sumByCatCountyWin$color <- colorVector[sumByCatCountyWin$Name]
#create a chlorealpleth map of the US
highchart() %>%
hc_add_series_map(
uscountygeojson,
sumByCatCountyWin,
value = "counts",
joinBy = c("fips", "COUNTY_FIP"),
name = "Cat Winner",
color = colorVector
) %>%
hc_mapNavigation(enabled = TRUE) %>%
hc_title(text = "Which Cat Wins Which County") %>%
hc_add_theme(hc_theme_smpl()) %>%
hc_legend(enabled = FALSE) %>%
hc_tooltip(pointFormat = "{point.name} is won by {point.Name}: {point.value} total tracks")
```
### Counties Won per Cat {data-width=250}
```{r}
# count the number of states that each cat won
total = sumByCatCountyWin[, .N, by = Name]
#create a ggplot bar chart of the number of states that each cat won
# label by the cat name
# color by the colorPalette
# order by the number of states won
g=ggplot(total, aes(x = Name, y = N, fill = Name)) +
geom_bar(stat = "identity") +
geom_text(aes(label = Name), vjust = -0.5, size = 3) +
scale_fill_manual(values = colorVector) +
labs(x = "Cat", y = "Number of Counties Won") +
theme_minimal() +
theme(legend.position = "none")
ggplotly(g)
```
Row {.tabset .tabset-fade}
-------------------------------------
### cats represented in the analysis
```{r}
kable(unique(df$OriginalName))
```
### cats not represented in the analysis
```{r}
kable(missingNames)
```